Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory143.8 KiB
Average record size in memory152.0 B

Variable types

Text3
Unsupported1
Categorical2
Numeric12

Alerts

Acousticness is highly overall correlated with EnergyHigh correlation
Danceability is highly overall correlated with ValenceHigh correlation
Energy is highly overall correlated with Acousticness and 1 other fieldsHigh correlation
Loudness is highly overall correlated with EnergyHigh correlation
Valence is highly overall correlated with DanceabilityHigh correlation
Time_Signature is highly imbalanced (82.8%)Imbalance
Duration is an unsupported type, check if it needs cleaning or further analysisUnsupported
Key has 136 (14.0%) zerosZeros
Instrumentalness has 282 (29.1%) zerosZeros
Popularity has 13 (1.3%) zerosZeros

Reproduction

Analysis started2024-09-10 17:56:20.478826
Analysis finished2024-09-10 17:56:39.043440
Duration18.56 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Track
Text

Distinct954
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:39.393322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length66
Median length47
Mean length18.160991
Min length2

Characters and Unicode

Total characters17598
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique939 ?
Unique (%)96.9%

Sample

1st rowAbc
2nd rowLet It Be
3rd rowI Want You Back
4th rowCecilia
5th rowSpirit In The Sky
ValueCountFrequency (%)
the 157
 
4.4%
you 126
 
3.5%
love 114
 
3.2%
to 79
 
2.2%
me 75
 
2.1%
i 70
 
2.0%
in 63
 
1.8%
a 52
 
1.5%
my 48
 
1.3%
of 44
 
1.2%
Other values (1120) 2728
76.7%
2024-09-10T14:56:39.838445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2587
 
14.7%
e 1658
 
9.4%
o 1255
 
7.1%
n 953
 
5.4%
a 817
 
4.6%
i 722
 
4.1%
t 680
 
3.9%
r 580
 
3.3%
h 508
 
2.9%
l 489
 
2.8%
Other values (68) 7349
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2587
 
14.7%
e 1658
 
9.4%
o 1255
 
7.1%
n 953
 
5.4%
a 817
 
4.6%
i 722
 
4.1%
t 680
 
3.9%
r 580
 
3.3%
h 508
 
2.9%
l 489
 
2.8%
Other values (68) 7349
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2587
 
14.7%
e 1658
 
9.4%
o 1255
 
7.1%
n 953
 
5.4%
a 817
 
4.6%
i 722
 
4.1%
t 680
 
3.9%
r 580
 
3.3%
h 508
 
2.9%
l 489
 
2.8%
Other values (68) 7349
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2587
 
14.7%
e 1658
 
9.4%
o 1255
 
7.1%
n 953
 
5.4%
a 817
 
4.6%
i 722
 
4.1%
t 680
 
3.9%
r 580
 
3.3%
h 508
 
2.9%
l 489
 
2.8%
Other values (68) 7349
41.8%

Artist
Text

Distinct526
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:40.105415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length51
Median length36
Mean length13.581011
Min length1

Characters and Unicode

Total characters13160
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique347 ?
Unique (%)35.8%

Sample

1st rowThe Jackson 5
2nd rowThe Beatles
3rd rowThe Jackson 5
4th rowSimon & Garfunkel
5th rowNorman Greenbaum
ValueCountFrequency (%)
the 167
 
7.3%
94
 
4.1%
john 38
 
1.7%
band 33
 
1.5%
and 27
 
1.2%
paul 23
 
1.0%
elton 16
 
0.7%
simon 15
 
0.7%
barry 15
 
0.7%
bee 14
 
0.6%
Other values (784) 1833
80.6%
2024-09-10T14:56:40.476958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1306
 
9.9%
e 1283
 
9.7%
a 914
 
6.9%
n 909
 
6.9%
r 783
 
5.9%
o 774
 
5.9%
i 718
 
5.5%
t 571
 
4.3%
l 552
 
4.2%
s 506
 
3.8%
Other values (57) 4844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1306
 
9.9%
e 1283
 
9.7%
a 914
 
6.9%
n 909
 
6.9%
r 783
 
5.9%
o 774
 
5.9%
i 718
 
5.5%
t 571
 
4.3%
l 552
 
4.2%
s 506
 
3.8%
Other values (57) 4844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1306
 
9.9%
e 1283
 
9.7%
a 914
 
6.9%
n 909
 
6.9%
r 783
 
5.9%
o 774
 
5.9%
i 718
 
5.5%
t 571
 
4.3%
l 552
 
4.2%
s 506
 
3.8%
Other values (57) 4844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1306
 
9.9%
e 1283
 
9.7%
a 914
 
6.9%
n 909
 
6.9%
r 783
 
5.9%
o 774
 
5.9%
i 718
 
5.5%
t 571
 
4.3%
l 552
 
4.2%
s 506
 
3.8%
Other values (57) 4844
36.8%

Duration
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size15.1 KiB

Time_Signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
4
914 
3
 
50
1
 
3
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters969
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Length

2024-09-10T14:56:40.583531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T14:56:40.687343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct491
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58872673
Minimum0.0942
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:40.782060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0942
5-th percentile0.304
Q10.488
median0.6
Q30.698
95-th percentile0.8246
Maximum0.985
Range0.8908
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.15752315
Coefficient of variation (CV)0.26756583
Kurtosis-0.27548805
Mean0.58872673
Median Absolute Deviation (MAD)0.103
Skewness-0.33951829
Sum570.4762
Variance0.024813544
MonotonicityNot monotonic
2024-09-10T14:56:40.886687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.637 9
 
0.9%
0.68 7
 
0.7%
0.665 7
 
0.7%
0.639 6
 
0.6%
0.669 6
 
0.6%
0.541 6
 
0.6%
0.565 6
 
0.6%
0.649 6
 
0.6%
0.671 6
 
0.6%
0.529 5
 
0.5%
Other values (481) 905
93.4%
ValueCountFrequency (%)
0.0942 1
0.1%
0.149 2
0.2%
0.16 1
0.1%
0.164 1
0.1%
0.185 1
0.1%
0.195 1
0.1%
0.203 1
0.1%
0.205 1
0.1%
0.207 1
0.1%
0.212 1
0.1%
ValueCountFrequency (%)
0.985 1
 
0.1%
0.965 1
 
0.1%
0.946 1
 
0.1%
0.925 1
 
0.1%
0.919 1
 
0.1%
0.912 3
0.3%
0.911 2
0.2%
0.908 1
 
0.1%
0.9 1
 
0.1%
0.889 1
 
0.1%

Energy
Real number (ℝ)

HIGH CORRELATION 

Distinct539
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58069455
Minimum0.00532
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:41.003448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00532
5-th percentile0.2478
Q10.428
median0.583
Q30.731
95-th percentile0.9056
Maximum0.995
Range0.98968
Interquartile range (IQR)0.303

Descriptive statistics

Standard deviation0.20207498
Coefficient of variation (CV)0.34798842
Kurtosis-0.57449804
Mean0.58069455
Median Absolute Deviation (MAD)0.151
Skewness-0.14444297
Sum562.69302
Variance0.040834297
MonotonicityNot monotonic
2024-09-10T14:56:41.113048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.673 7
 
0.7%
0.528 7
 
0.7%
0.641 6
 
0.6%
0.644 6
 
0.6%
0.409 5
 
0.5%
0.56 5
 
0.5%
0.532 5
 
0.5%
0.626 4
 
0.4%
0.573 4
 
0.4%
0.492 4
 
0.4%
Other values (529) 916
94.5%
ValueCountFrequency (%)
0.00532 1
0.1%
0.0088 1
0.1%
0.0264 1
0.1%
0.0265 1
0.1%
0.0751 1
0.1%
0.0803 1
0.1%
0.0809 1
0.1%
0.0897 1
0.1%
0.112 1
0.1%
0.116 1
0.1%
ValueCountFrequency (%)
0.995 2
0.2%
0.989 1
0.1%
0.987 1
0.1%
0.98 1
0.1%
0.979 1
0.1%
0.974 1
0.1%
0.969 1
0.1%
0.968 2
0.2%
0.961 1
0.1%
0.957 1
0.1%

Key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2053664
Minimum0
Maximum11
Zeros136
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:41.205650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5701547
Coefficient of variation (CV)0.6858604
Kurtosis-1.2930395
Mean5.2053664
Median Absolute Deviation (MAD)3
Skewness-0.015580843
Sum5044
Variance12.746004
MonotonicityNot monotonic
2024-09-10T14:56:41.294866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 136
14.0%
7 118
12.2%
9 115
11.9%
2 100
10.3%
5 95
9.8%
4 81
8.4%
1 73
7.5%
10 64
6.6%
11 64
6.6%
8 52
 
5.4%
Other values (2) 71
7.3%
ValueCountFrequency (%)
0 136
14.0%
1 73
7.5%
2 100
10.3%
3 25
 
2.6%
4 81
8.4%
5 95
9.8%
6 46
 
4.7%
7 118
12.2%
8 52
 
5.4%
9 115
11.9%
ValueCountFrequency (%)
11 64
6.6%
10 64
6.6%
9 115
11.9%
8 52
5.4%
7 118
12.2%
6 46
 
4.7%
5 95
9.8%
4 81
8.4%
3 25
 
2.6%
2 100
10.3%

Loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct907
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.8814272
Minimum-31.646
Maximum-2.34
Zeros0
Zeros (%)0.0%
Negative969
Negative (%)100.0%
Memory size15.1 KiB
2024-09-10T14:56:41.388494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-31.646
5-th percentile-15.7326
Q1-12.36
median-9.566
Q3-7.111
95-th percentile-4.6884
Maximum-2.34
Range29.306
Interquartile range (IQR)5.249

Descriptive statistics

Standard deviation3.7234125
Coefficient of variation (CV)-0.37680918
Kurtosis2.6759072
Mean-9.8814272
Median Absolute Deviation (MAD)2.588
Skewness-0.94540047
Sum-9575.103
Variance13.863801
MonotonicityNot monotonic
2024-09-10T14:56:41.494083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.472 3
 
0.3%
-10.834 2
 
0.2%
-13.119 2
 
0.2%
-4.653 2
 
0.2%
-9.138 2
 
0.2%
-7.246 2
 
0.2%
-12.923 2
 
0.2%
-10.518 2
 
0.2%
-8.752 2
 
0.2%
-8.555 2
 
0.2%
Other values (897) 948
97.8%
ValueCountFrequency (%)
-31.646 1
0.1%
-30 1
0.1%
-27.103 1
0.1%
-27.09 1
0.1%
-26.128 1
0.1%
-23.56 1
0.1%
-21.657 1
0.1%
-21.644 1
0.1%
-20.518 1
0.1%
-20.439 1
0.1%
ValueCountFrequency (%)
-2.34 1
0.1%
-2.515 1
0.1%
-2.588 1
0.1%
-2.621 1
0.1%
-2.785 1
0.1%
-3.081 1
0.1%
-3.144 1
0.1%
-3.222 1
0.1%
-3.226 1
0.1%
-3.471 1
0.1%

Mode
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
1
735 
0
234 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters969
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Length

2024-09-10T14:56:41.595396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T14:56:41.668932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Most occurring characters

ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Speechiness
Real number (ℝ)

Distinct453
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06008937
Minimum0.0232
Maximum0.737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:41.748675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.02674
Q10.0313
median0.0383
Q30.0568
95-th percentile0.1806
Maximum0.737
Range0.7138
Interquartile range (IQR)0.0255

Descriptive statistics

Standard deviation0.065842021
Coefficient of variation (CV)1.0957349
Kurtosis25.310909
Mean0.06008937
Median Absolute Deviation (MAD)0.0095
Skewness4.3845087
Sum58.2266
Variance0.0043351717
MonotonicityNot monotonic
2024-09-10T14:56:41.860699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0336 9
 
0.9%
0.0346 9
 
0.9%
0.0302 8
 
0.8%
0.0287 8
 
0.8%
0.0283 8
 
0.8%
0.0341 8
 
0.8%
0.0369 7
 
0.7%
0.0282 7
 
0.7%
0.0325 7
 
0.7%
0.0298 7
 
0.7%
Other values (443) 891
92.0%
ValueCountFrequency (%)
0.0232 1
 
0.1%
0.0239 1
 
0.1%
0.024 2
0.2%
0.0241 1
 
0.1%
0.0243 2
0.2%
0.0245 1
 
0.1%
0.0246 2
0.2%
0.0247 1
 
0.1%
0.0248 4
0.4%
0.0249 3
0.3%
ValueCountFrequency (%)
0.737 1
0.1%
0.576 1
0.1%
0.467 1
0.1%
0.457 1
0.1%
0.452 1
0.1%
0.448 1
0.1%
0.405 2
0.2%
0.368 1
0.1%
0.364 1
0.1%
0.361 1
0.1%

Acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct712
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33370655
Minimum2.23 × 10-5
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:41.976296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.23 × 10-5
5-th percentile0.006
Q10.0801
median0.272
Q30.544
95-th percentile0.8594
Maximum0.996
Range0.9959777
Interquartile range (IQR)0.4639

Descriptive statistics

Standard deviation0.27987703
Coefficient of variation (CV)0.83869205
Kurtosis-0.84135273
Mean0.33370655
Median Absolute Deviation (MAD)0.2153
Skewness0.59184928
Sum323.36165
Variance0.078331154
MonotonicityNot monotonic
2024-09-10T14:56:42.089215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.357 8
 
0.8%
0.309 5
 
0.5%
0.305 5
 
0.5%
0.484 5
 
0.5%
0.181 5
 
0.5%
0.0185 4
 
0.4%
0.22 4
 
0.4%
0.524 4
 
0.4%
0.122 4
 
0.4%
0.81 4
 
0.4%
Other values (702) 921
95.0%
ValueCountFrequency (%)
2.23 × 10-51
0.1%
0.000109 1
0.1%
0.000133 1
0.1%
0.000215 1
0.1%
0.000261 1
0.1%
0.00028 1
0.1%
0.000288 1
0.1%
0.000385 1
0.1%
0.000598 1
0.1%
0.000668 2
0.2%
ValueCountFrequency (%)
0.996 1
0.1%
0.994 1
0.1%
0.992 1
0.1%
0.983 1
0.1%
0.973 1
0.1%
0.971 1
0.1%
0.965 1
0.1%
0.959 1
0.1%
0.953 1
0.1%
0.95 1
0.1%

Instrumentalness
Real number (ℝ)

ZEROS 

Distinct609
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046836978
Minimum0
Maximum0.97
Zeros282
Zeros (%)29.1%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:42.494554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.07 × 10-5
Q30.00275
95-th percentile0.3212
Maximum0.97
Range0.97
Interquartile range (IQR)0.00275

Descriptive statistics

Standard deviation0.16308138
Coefficient of variation (CV)3.4818938
Kurtosis17.744921
Mean0.046836978
Median Absolute Deviation (MAD)5.07 × 10-5
Skewness4.242706
Sum45.385031
Variance0.026595537
MonotonicityNot monotonic
2024-09-10T14:56:42.598357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 282
29.1%
0.000122 4
 
0.4%
1.81 × 10-64
 
0.4%
0.00141 3
 
0.3%
0.00031 3
 
0.3%
0.000171 3
 
0.3%
0.00014 3
 
0.3%
1.1 × 10-62
 
0.2%
0.00192 2
 
0.2%
1.68 × 10-62
 
0.2%
Other values (599) 661
68.2%
ValueCountFrequency (%)
0 282
29.1%
1 × 10-61
 
0.1%
1.08 × 10-61
 
0.1%
1.09 × 10-61
 
0.1%
1.1 × 10-62
 
0.2%
1.2 × 10-61
 
0.1%
1.22 × 10-61
 
0.1%
1.23 × 10-61
 
0.1%
1.28 × 10-61
 
0.1%
1.31 × 10-62
 
0.2%
ValueCountFrequency (%)
0.97 1
0.1%
0.968 1
0.1%
0.963 1
0.1%
0.959 2
0.2%
0.944 1
0.1%
0.94 2
0.2%
0.92 1
0.1%
0.916 1
0.1%
0.912 1
0.1%
0.909 1
0.1%

Liveness
Real number (ℝ)

Distinct527
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17538091
Minimum0.015
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:42.707728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.015
5-th percentile0.0481
Q10.0863
median0.119
Q30.197
95-th percentile0.541
Maximum0.985
Range0.97
Interquartile range (IQR)0.1107

Descriptive statistics

Standard deviation0.15421854
Coefficient of variation (CV)0.87933482
Kurtosis6.3748166
Mean0.17538091
Median Absolute Deviation (MAD)0.0433
Skewness2.3733015
Sum169.9441
Variance0.023783358
MonotonicityNot monotonic
2024-09-10T14:56:42.819649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.105 12
 
1.2%
0.108 11
 
1.1%
0.103 10
 
1.0%
0.113 10
 
1.0%
0.122 9
 
0.9%
0.115 9
 
0.9%
0.12 9
 
0.9%
0.114 9
 
0.9%
0.109 8
 
0.8%
0.104 8
 
0.8%
Other values (517) 874
90.2%
ValueCountFrequency (%)
0.015 1
0.1%
0.0166 1
0.1%
0.0188 1
0.1%
0.0199 1
0.1%
0.0295 2
0.2%
0.0309 1
0.1%
0.0318 1
0.1%
0.032 1
0.1%
0.0339 1
0.1%
0.034 1
0.1%
ValueCountFrequency (%)
0.985 1
0.1%
0.974 1
0.1%
0.962 1
0.1%
0.957 1
0.1%
0.935 1
0.1%
0.9 1
0.1%
0.892 1
0.1%
0.805 1
0.1%
0.792 1
0.1%
0.779 1
0.1%

Valence
Real number (ℝ)

HIGH CORRELATION 

Distinct568
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62251312
Minimum1 × 10-5
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:42.931064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.1762
Q10.423
median0.652
Q30.846
95-th percentile0.962
Maximum0.989
Range0.98899
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.25223747
Coefficient of variation (CV)0.40519222
Kurtosis-0.9403716
Mean0.62251312
Median Absolute Deviation (MAD)0.206
Skewness-0.40159539
Sum603.21521
Variance0.063623741
MonotonicityNot monotonic
2024-09-10T14:56:43.042653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.962 9
 
0.9%
0.963 8
 
0.8%
0.969 6
 
0.6%
0.971 6
 
0.6%
0.961 5
 
0.5%
0.826 5
 
0.5%
0.718 5
 
0.5%
0.967 5
 
0.5%
0.926 5
 
0.5%
0.89 4
 
0.4%
Other values (558) 911
94.0%
ValueCountFrequency (%)
1 × 10-51
0.1%
0.0346 1
0.1%
0.0348 1
0.1%
0.0385 1
0.1%
0.0393 1
0.1%
0.0397 1
0.1%
0.0558 1
0.1%
0.0579 1
0.1%
0.0589 1
0.1%
0.0685 2
0.2%
ValueCountFrequency (%)
0.989 1
 
0.1%
0.985 1
 
0.1%
0.981 1
 
0.1%
0.979 1
 
0.1%
0.978 1
 
0.1%
0.973 1
 
0.1%
0.972 1
 
0.1%
0.971 6
0.6%
0.97 2
 
0.2%
0.969 6
0.6%

Tempo
Real number (ℝ)

Distinct944
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.87407
Minimum53.986
Maximum211.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:43.143307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53.986
5-th percentile79.1642
Q199.975
median117.401
Q3134.004
95-th percentile170.9286
Maximum211.27
Range157.284
Interquartile range (IQR)34.029

Descriptive statistics

Standard deviation27.03621
Coefficient of variation (CV)0.22743573
Kurtosis0.39551209
Mean118.87407
Median Absolute Deviation (MAD)17.074
Skewness0.5854845
Sum115188.97
Variance730.95668
MonotonicityNot monotonic
2024-09-10T14:56:43.247889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.977 3
 
0.3%
148.063 2
 
0.2%
120.157 2
 
0.2%
166.139 2
 
0.2%
79.764 2
 
0.2%
95.048 2
 
0.2%
132.642 2
 
0.2%
130.166 2
 
0.2%
85.126 2
 
0.2%
103.01 2
 
0.2%
Other values (934) 948
97.8%
ValueCountFrequency (%)
53.986 1
0.1%
61.53 1
0.1%
62.204 1
0.1%
63.059 1
0.1%
65.09 1
0.1%
65.832 1
0.1%
65.861 1
0.1%
67.006 1
0.1%
68.482 1
0.1%
68.69 1
0.1%
ValueCountFrequency (%)
211.27 1
0.1%
207.266 1
0.1%
205.845 1
0.1%
205.747 1
0.1%
203.812 1
0.1%
202.297 1
0.1%
202.14 1
0.1%
201.467 1
0.1%
200.813 1
0.1%
200.423 1
0.1%

Popularity
Real number (ℝ)

ZEROS 

Distinct87
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.623323
Minimum0
Maximum90
Zeros13
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:43.358535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q144
median56
Q367
95-th percentile78
Maximum90
Range90
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.989193
Coefficient of variation (CV)0.3354733
Kurtosis0.60997678
Mean53.623323
Median Absolute Deviation (MAD)11
Skewness-0.81688113
Sum51961
Variance323.61107
MonotonicityNot monotonic
2024-09-10T14:56:43.464068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 31
 
3.2%
64 31
 
3.2%
55 30
 
3.1%
49 26
 
2.7%
74 26
 
2.7%
51 25
 
2.6%
71 25
 
2.6%
47 25
 
2.6%
67 25
 
2.6%
62 24
 
2.5%
Other values (77) 701
72.3%
ValueCountFrequency (%)
0 13
1.3%
1 2
 
0.2%
2 2
 
0.2%
3 1
 
0.1%
4 3
 
0.3%
5 2
 
0.2%
6 2
 
0.2%
7 4
 
0.4%
8 1
 
0.1%
9 2
 
0.2%
ValueCountFrequency (%)
90 2
 
0.2%
89 1
 
0.1%
86 3
 
0.3%
85 3
 
0.3%
84 5
0.5%
83 6
0.6%
82 4
 
0.4%
81 9
0.9%
80 10
1.0%
79 4
 
0.4%

Year
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.5707
Minimum1970
Maximum1979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:43.552603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1970
Q11972
median1975
Q31977
95-th percentile1979
Maximum1979
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8450711
Coefficient of variation (CV)0.0014408555
Kurtosis-1.2061454
Mean1974.5707
Median Absolute Deviation (MAD)2
Skewness-0.020943093
Sum1913359
Variance8.0944294
MonotonicityIncreasing
2024-09-10T14:56:43.632647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1973 100
10.3%
1974 99
10.2%
1978 99
10.2%
1976 99
10.2%
1977 99
10.2%
1979 98
10.1%
1975 97
10.0%
1972 97
10.0%
1971 93
9.6%
1970 88
9.1%
ValueCountFrequency (%)
1970 88
9.1%
1971 93
9.6%
1972 97
10.0%
1973 100
10.3%
1974 99
10.2%
1975 97
10.0%
1976 99
10.2%
1977 99
10.2%
1978 99
10.2%
1979 98
10.1%
ValueCountFrequency (%)
1979 98
10.1%
1978 99
10.2%
1977 99
10.2%
1976 99
10.2%
1975 97
10.0%
1974 99
10.2%
1973 100
10.3%
1972 97
10.0%
1971 93
9.6%
1970 88
9.1%
Distinct952
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-10T14:56:43.828190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length58
Median length41
Mean length15.373581
Min length0

Characters and Unicode

Total characters14897
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique936 ?
Unique (%)96.6%

Sample

1st rowAbc
2nd rowLet
3rd row Want You Back
4th rowCecilia
5th rowSpirit Sky
ValueCountFrequency (%)
you 126
 
4.9%
love 114
 
4.4%
38
 
1.5%
get 26
 
1.0%
just 18
 
0.7%
like 18
 
0.7%
up 18
 
0.7%
night 17
 
0.7%
woman 17
 
0.7%
way 17
 
0.7%
Other values (1078) 2161
84.1%
2024-09-10T14:56:44.200587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2587
17.4%
e 1348
 
9.0%
o 1061
 
7.1%
a 780
 
5.2%
n 750
 
5.0%
i 683
 
4.6%
t 544
 
3.7%
r 521
 
3.5%
l 459
 
3.1%
s 365
 
2.5%
Other values (67) 5799
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2587
17.4%
e 1348
 
9.0%
o 1061
 
7.1%
a 780
 
5.2%
n 750
 
5.0%
i 683
 
4.6%
t 544
 
3.7%
r 521
 
3.5%
l 459
 
3.1%
s 365
 
2.5%
Other values (67) 5799
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2587
17.4%
e 1348
 
9.0%
o 1061
 
7.1%
a 780
 
5.2%
n 750
 
5.0%
i 683
 
4.6%
t 544
 
3.7%
r 521
 
3.5%
l 459
 
3.1%
s 365
 
2.5%
Other values (67) 5799
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2587
17.4%
e 1348
 
9.0%
o 1061
 
7.1%
a 780
 
5.2%
n 750
 
5.0%
i 683
 
4.6%
t 544
 
3.7%
r 521
 
3.5%
l 459
 
3.1%
s 365
 
2.5%
Other values (67) 5799
38.9%

Interactions

2024-09-10T14:56:37.630288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:21.851938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:25.578085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.387857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.379383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.440238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.387796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.478360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.474466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.434059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.663275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.663115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.710133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:22.169417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:25.880152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.459239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.476573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.521857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.471334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.563986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.553615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.510514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.741071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.737834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.788454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:22.461205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:26.212517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.535866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.575136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.598872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.558868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.644822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.631787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.588867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.821900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.817943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.864991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:22.753667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:27.141465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.602958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.681115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.684368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.645399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.731709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.710829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.669393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.905357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.897436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.951132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:23.042053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:27.463609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.681367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.778178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.759198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.737942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.818494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.790946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.751359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.988779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.980294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:38.033739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:23.409727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:27.805203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.757996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.856718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.849934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.827775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.909498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.881587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.118227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.080356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.067240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:38.108818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:23.739252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:27.903336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.837728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.934284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.926845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.946322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.997156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.959142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.188233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.164926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.147645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:38.188620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:24.043022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:27.991188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.913217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.022060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.004894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.063049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.076892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.036282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.266760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.246644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.230634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:38.265330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:24.341979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.062775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.995918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.101068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.079781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.143346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.156618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.113490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.349025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.330180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.307728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:38.343862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:24.637846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.141443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.066664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.185118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.155533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.227880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.235155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.190494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.423207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.415190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.389542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:38.437550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:24.980696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.232169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.141328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.278773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.236112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.316887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.319883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.277935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.507213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.501906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.477151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:38.513916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:25.270983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:28.305541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:29.294854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:30.358294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:31.308584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:32.398890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:33.394984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:34.353528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:35.582728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:36.579572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T14:56:37.554750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-10T14:56:44.287124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AcousticnessDanceabilityEnergyInstrumentalnessKeyLivenessLoudnessModePopularitySpeechinessTempoTime_SignatureValenceYear
Acousticness1.000-0.250-0.563-0.0330.0380.057-0.3700.138-0.120-0.188-0.1060.185-0.211-0.115
Danceability-0.2501.0000.209-0.0170.008-0.2240.0750.0880.0960.232-0.1110.1420.5300.122
Energy-0.5630.2091.000-0.000-0.0490.0580.6550.0630.0800.3290.1480.1710.3960.008
Instrumentalness-0.033-0.017-0.0001.0000.029-0.080-0.1800.1120.003-0.080-0.0540.117-0.009-0.009
Key0.0380.008-0.0490.0291.0000.069-0.0740.207-0.042-0.001-0.0180.000-0.018-0.040
Liveness0.057-0.2240.058-0.0800.0691.0000.0790.022-0.0270.025-0.0200.018-0.140-0.008
Loudness-0.3700.0750.655-0.180-0.0740.0791.0000.0140.1620.1540.0680.4130.0340.035
Mode0.1380.0880.0630.1120.2070.0220.0141.0000.0820.0410.0000.0510.0000.061
Popularity-0.1200.0960.0800.003-0.042-0.0270.1620.0821.0000.0230.0220.053-0.0300.136
Speechiness-0.1880.2320.329-0.080-0.0010.0250.1540.0410.0231.0000.1270.0000.092-0.055
Tempo-0.106-0.1110.148-0.054-0.018-0.0200.0680.0000.0220.1271.0000.1260.0670.034
Time_Signature0.1850.1420.1710.1170.0000.0180.4130.0510.0530.0000.1261.0000.1660.035
Valence-0.2110.5300.396-0.009-0.018-0.1400.0340.000-0.0300.0920.0670.1661.000-0.034
Year-0.1150.1220.008-0.009-0.040-0.0080.0350.0610.136-0.0550.0340.035-0.0341.000

Missing values

2024-09-10T14:56:38.634235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-10T14:56:38.867005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TrackArtistDurationTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYearTrack Transformed
0AbcThe Jackson 5[2, 42]40.6820.9263-2.51500.06070.0404000.0000000.19000.860105.969811970Abc
1Let It BeThe Beatles[4, 03]40.4430.4030-8.33910.03220.6310000.0000000.11100.410143.462781970Let
2I Want You BackThe Jackson 5[2, 56]40.4690.5388-13.55910.05750.3050000.0001140.37000.885196.606781970Want You Back
3CeciliaSimon & Garfunkel[2, 54]40.7550.8760-8.86710.03620.3570000.0000050.22000.954102.762761970Cecilia
4Spirit In The SkyNorman Greenbaum[4, 02]40.6090.6179-7.09110.03070.0994000.0040400.11800.543128.903751970Spirit Sky
5Love Grows (WHERE My Rosemary Goes)Edison Lighthouse[2, 54]40.5680.8249-4.61310.02990.4030000.0000000.08550.753108.625731970Love Grows (WHERE Rosemary Goes)
6The LetterJoe Cocker[1, 31]40.7210.4968-6.29610.06450.1400000.0001010.10200.18081.499721970Letter
7The House Of The Rising SunFrijid Pink[4, 31]30.2950.5849-6.69600.03450.0003850.2180000.09960.228117.200711970House Rising Sun
8Fire And RainJames Taylor[3, 23]40.5970.2715-17.29310.03940.7660000.0119000.09330.33876.271711970Fire Rain
9In The SummertimeMungo Jerry[3, 31]40.7540.4494-14.01310.06150.7240000.0000000.16200.97382.751711970Summertime
TrackArtistDurationTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYearTrack Transformed
969A Little More LoveOlivia Newton-John[3, 27]40.7170.4148-14.85510.03640.029000.0094400.08650.494100.178391979Little More Love
970In The NavyVillage People[3, 45]40.7590.8897-10.59200.05020.125000.0000000.04100.886126.201381979Navy
971Mama Can’t Buy You LoveElton John[4, 04]40.5290.4325-14.24510.03330.524000.0000000.11500.55594.382361979Mama Can’t Buy You Love
972Goodnight TonightPaul McCartney & Wings[4, 20]40.7480.6831-9.88500.04660.056600.0006390.08090.943123.385351979Goodnight Tonight
973We’ve Got TonightBob Seger & The Silver Bullet Band[3, 35]40.3790.3878-9.28310.02780.757000.0000000.10300.22261.530261979Got Tonight
975He’s The Greatest DancerSister Sledge[6, 15]40.7000.8157-9.71100.04400.001150.0012400.09010.837113.245141979Greatest Dancer
976Don’t Cry Out LoudMelissa Manchester[2, 15]40.2980.2520-8.95010.03390.901000.0000090.12700.19390.95591979Cry Out Loud
977When You’re In Love With A Beautiful WomanDr. Hook[2, 54]40.6650.6638-11.36710.03860.485000.0068200.15700.792110.65671979Love Beautiful Woman
978I’ll Never Love This Way AgainDionne Warwick[2, 58]40.4520.4348-8.87010.03990.792000.0139000.16500.247137.70251979Never Love Way Again
979Dim All The NightsDonna Summer[4, 08]40.7580.5407-10.91110.03850.055100.0000000.03430.661121.58101979Dim All Nights